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OnePose Demo on Custom Data (WIP)

In this tutorial we introduce the demo of OnePose running with data captured with our OnePose Cap application available for iOS device. The app is still under preparing for release. However, you can try it with the sample data and skip the first step.

Step 1: Capture the mapping sequence and the test sequence with OnePose Cap.

The app is under brewing🍺 coming soon.

Step 2: Organize the file structure of collected sequences

  1. Export the collected mapping sequence and the test sequence to the PC.
  2. Rename the annotate and test sequences directories to your_obj_name-annotate and your_obj_name-test respectively and organize the data as the follow structure:
    |--- /your/path/to/scanned_data
    |       |--- your_obj_name
    |       |       |---your_obj_name-annotate
    |       |       |---your_obj_name-test
    
    Refer to the sample data as an example.
  3. Link the collected data to the project directory
    REPO_ROOT=/path/to/OnePose
    ln -s /path/to/scanned_data $REPO_ROOT/data/demo

Now the data is prepared!

Step 3: Run OnePose with collected data

Download the pretrained OnePose model and move it to ${REPO_ROOT}/data/model/checkpoints/onepose/GATsSPG.ckpt.

[Optional] To run OnePose with tracking modeule, pelase install DeepLM. Please make sure the sample program in DeepLM can be correctly executed to ensure successful installation.

Execute the following commands, and a demo video naming demo_video.mp4 will be saved in the folder of the test sequence.

REPO_ROOT=/path/to/OnePose
OBJ_NAME=your_obj_name

cd $REPO_ROOT
conda activate OnePose

bash scripts/demo_pipeline.sh $OBJ_NAME

# [Optional] running OnePose with tracking
export PYTHONPATH=$PYTHONPATH:/path/to/DeepLM/build
export TORCH_USE_RTLD_GLOBAL=YES

bash scripts/demo_pipeline.sh $OBJ_NAME --WITH_TRACKING